AlphaGo Zero & AlphaZero

However, in October 2017, AlphaGo Zero, an evolution of AlphaGo was introduced. While previous versions were initially trained on thousands of human amateur and professional games to learn how to play Go, AlphaGo Zero learns exclusively by playing games against itself, starting from completely random play, to quickly surpass human level of play and defeated the previously published champion-defeating version of AlphaGo by 100 games to 0 [29][30]. AlphaGo Zero was further improved and even generalized for other games now dubbed AlphaZero, as published in December 2017 [31].

Quotes

There is no significant difference between an alpha-beta search with heavy LMR and a static evaluator (current state of the art in chess) and an UCT searcher with a small exploration constant that does playouts (state of the art in go).

The shape of the tree they search is very similar. The main breakthrough in Go the last few years was how to backup an uncertain Monte Carlo score. This was solved. For chess this same problem was solved around the time quiescent search was developed.

Both are producing strong programs and we've proven for both the methods that they scale in strength as hardware speed goes up.

So I would say that we've successfully adopted the simple, brute force methods for chess to Go and they already work without increases in computer speed. The increases will make them progressively stronger though, and with further software tweaks they will eventually surpass humans.